Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs

Muhammad Mansoor, Francesco Grimaccia, Sonia Leva and Marco Mussetta

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 184, issue C, 282-293

Abstract: The electrical load forecasting is a fundamental technique for consumer load prediction for utilities. The accurate load forecasting is crucial to design Demand Response (DR) programs in the paradigm of smart grids. Artificial Neural Network (ANN) based techniques have been widely used in recent years and applied to predict the electric load with high accuracy to participate in DR programs for commercial, industrial and residential consumers. This research work is focused on the use and comparison of two ANN-based load forecasting techniques, i.e. Feed-Forward Neural Network (FFNN) and Echo State Network (ESN), on a dataset related to commercial buildings, in view of a possible DR program application. The results of both models are compared based on the load forecasting accuracy through experimental measurements and suitably defined metrics.

Keywords: Load forecasting; Neural network; Echo state network; Demand response programs (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:184:y:2021:i:c:p:282-293

DOI: 10.1016/j.matcom.2020.07.011

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